148 lines
4.9 KiB
Python
148 lines
4.9 KiB
Python
import face_recognition
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import numpy as np
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import cv2
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import base64
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from PIL import Image, ImageDraw, ImageFont
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class FaceNotFoundException(Exception):
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def __init__(self):
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err = "Compare Failure,cannot find face in image"
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Exception.__init__(self, err)
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# base64转opencv图片
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def convert_to_image(base64_code):
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"""
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base64转图片
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:param base64_code:需要转换的base64码:
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:return image:openCv格式图片:
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"""
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# base64解码
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b64image = base64.b64decode(base64_code)
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# 转opencv图片
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image_array = np.frombuffer(b64image, np.uint8)
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result_image = cv2.imdecode(image_array, cv2.COLOR_BGR2RGB)
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# 返回opencv图片对象
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if result_image is None:
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return "error"
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else:
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return result_image
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# 缩放图片
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def resize_image(image, multiple=1.0):
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"""
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缩放图片
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:param image:需要缩放的图片:
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:param multiple:可选缩放倍数,默认120大小基准缩放:
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:return:
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"""
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# 默认以基准120大小等比例缩小
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base_height = 120
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image_height = image.shape[0]
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if multiple == 1:
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multiple = base_height / image_height
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result_image = cv2.resize(image, (0, 0), fx=multiple, fy=multiple)
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return result_image
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# 获取单个人脸128特征点向量
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def get_face_encoding(face_image, face_locations=None):
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"""
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获取人脸特征点向量
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:param face_image: 已知人脸图片:
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:param face_locations: 人脸位置:
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:return face_encoding: 人脸特征点向量:
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"""
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face_encoding = face_recognition.face_encodings(face_image, face_locations)
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return face_encoding
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# 人脸比对
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def contrast_faces(known_image, unknown_image, tolerance=0.4):
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"""
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人脸比对
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:param known_image: 已知图片:
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:param unknown_image: 待检测图片:
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:param tolerance: 检测基准,数值越小结果越精确,同时可能出现比对失败:
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:return result:检测结果:
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"""
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# noinspection PyBroadException
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try:
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known_image_encoding = get_face_encoding(resize_image(known_image))[0]
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unknown_image_encoding = get_face_encoding(resize_image(unknown_image))[0]
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result = face_recognition.compare_faces([known_image_encoding], unknown_image_encoding, tolerance)
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return result
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except IndexError:
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return "nf"
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except Exception as e:
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print("recognition error！\n cause：{}".format(e))
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return "err"
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# 摄像头实时比对
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def real_time_comparison(known_face_encodings, known_face_names, tolerance=0.4):
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"""
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人脸实时比对
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:param known_face_encodings:已知人脸特征点向量数组:
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:param known_face_names:已知人脸名称:
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:param tolerance:检测基准,数值越小结果越精确,同时可能出现比对失败:
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:return None:无返回值:
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"""
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process_this_frame = True
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face_locations = ""
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face_names = ""
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cap = cv2.VideoCapture(0)
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while True:
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ret, frame = cap.read()
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frame = cv2.flip(frame, 1, dst=None)
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small_frame = resize_image(frame, 0.5)
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rgb_small_frame = small_frame[:, :, ::-1]
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if process_this_frame:
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face_locations = face_recognition.face_locations(rgb_small_frame)
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face_encodings = get_face_encoding(rgb_small_frame, face_locations)
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face_names = []
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for face_encoding in face_encodings:
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matches = face_recognition.compare_faces(known_face_encodings, face_encoding, tolerance)
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name = "Unknown"
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mmax_index = matches.index(max(matches))
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mmax_value = matches[mmax_index]
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for index, match in enumerate(matches):
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matches[index] = False
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if mmax_value > (1-tolerance)*100:
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matches[mmax_index] = True
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if True in matches:
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first_match_index = matches.index(True)
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name = known_face_names[first_match_index]
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face_names.append(name)
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process_this_frame = not process_this_frame
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for (top, right, bottom, left), name in zip(face_locations, face_names):
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top *= 2
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right *= 2
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bottom *= 2
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left *= 2
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cv2.rectangle(frame, (left - 10, top - 10), (right + 10, bottom + 10), (0, 0, 255), 1)
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cv2.rectangle(frame, (left, bottom - 20), (right, bottom), (0, 0, 100), cv2.FILLED)
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cv2_im = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
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pil_im = Image.fromarray(cv2_im)
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draw = ImageDraw.Draw(pil_im)
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font = ImageFont.truetype("simhei.ttf", 25, encoding="utf-8")
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draw.text((left + 20, bottom - 25), name, (255, 255, 255), font=font)
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frame = cv2.cvtColor(np.array(pil_im), cv2.COLOR_RGB2BGR)
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cv2.imshow('Video', frame)
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if cv2.waitKey(1) & 0xFF == ord('q'):
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break
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cap.release()
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cv2.destroyAllWindows()
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